AI writes the code and the queries. The graph checks them before merge: deterministically, not by another model's opinion.
A function calls a function. A service reads a table. A task changes a module. When everything lives in one graph, "what breaks if I change this?" is answered by a query, not by a guess.
Functions, modules, tables, columns, queries, tasks, pull requests.
Calls, imports, reads, writes, depends on, changed by.
Complexity, owner, size, type, sensitivity, last change, who approved it.
An executor that writes, and two controllers that know the code and the data.
A deterministic graph of your code (GoCPG, CodeGraph). The engine computes the findings, not an LLM.
Schemas, relations and lineage in one graph, native to Postgres. Built by the Postgres Pro team.
Orchestrator, executors and reviewers that take a ticket to a reviewed change. Every change is checked against the graph before merge.
Tests pass. The architecture rule fails. That is the moment we exist for.
AI executors write requirements, plans, code, tests and docs. The code graph checks architecture, security and quality on every change. Management sees deadlines, dependencies, risks and release readiness across the whole portfolio, not per team.
Five managed stages: goal and acceptance criteria, plan with affected systems, execution by people and AI, automated checks, a release decision with visible risks.
Calls, imports, data access and ownership in one graph. The question "what does this change touch?" gets an answer with a path you can open, not an estimate.
Git, your tracker, CI/CD, test systems, documentation and the AI assistants your engineers already use. One more check in the pipeline, not a new platform to migrate to.
Scenario figures are pilot hypotheses, measured on your repositories before any commercial commitment.
Moving off Oracle, splitting a processing contour by geography, leaving Greenplum for Greengage or Cloudberry: the risk is not in the data volume, it is in the dependencies nobody has mapped. We put schemas, code, queries and data flows into one graph and check every step of the migration against it.
Which application code, procedures, reports and ETL jobs read and write each table and column. Dead objects, hidden coupling, the order in which contours can be cut over.
AI rewrites PL/SQL and queries and generates migrations. The graph checks them before rollout: type and semantic mismatches, broken lineage, replication and CDC paths that would lose data.
Every schema change shows its impact before it is made. Analytical agents work on top of the graph and bring back answers, not raw dumps, with a path to the source.
Banks and large enterprises are moving core contours to Postgres: terabytes of updates a day, analytics with no more than a day of lag, zero tolerance for downtime or data loss. These programs are budgeted in the hundreds of millions to billions of rubles, and most of the cost is risk and rework.
The team comes from Postgres Pro and Yandex Managed PostgreSQL and has taken part in large bank migrations to Postgres Pro first-hand. We know how these projects fail and where the graph saves weeks.
Early access: we are taking two or three design partners with an active migration program.
Orchestrator, executors and reviewers take a ticket from spec to a reviewed change, in the messenger your team already uses. The difference from a vendor IDE agent is the economics: every token is accounted for, budgeted and routed to the cheapest model that can do the step.
Every call is attributed to a ticket, a stage, a role and a model: input, output, cache reads and writes separately. You see what a feature cost, not just the monthly invoice.
Limits per team, project and task. Near the limit Rabotnik compacts the session, switches to a cheaper model or pauses, instead of failing mid-task. FinOps for inference.
Sessions are compacted and kept warm in the prompt cache, so a repeat costs a cache read. With the code graph the agent reads the nodes it needs instead of whole files.
A frontier model writes the spec and reviews; a cheaper or local model executes. A vendor harness keeps every step on its own models. Rabotnik is not tied to a vendor: Anthropic, OpenAI, open-weight models on your own GPUs, in one task.
AI work scales with tasks, not headcount. One inference budget for the team, on the provider or hardware you choose, inside your perimeter if needed.
Real ledger of one ticket from our own pipeline (T-724, October 2026), priced at public API list rates. Rabotnik: $6.36. The same tokens on Opus 5.5 alone: $22.96 (3.6x), on GPT-6 Sol alone: $12.38 (1.9x). The local Qwen runs on our own GPU and costs no API fee. A single frontier model may need fewer retries, so in the pilot we measure the same task both ways.
You describe what you need in the chat or a ticket.
Spec, code and tests, on your models.
Code and data rules run on the change, deterministically.
A clean change with evidence, ready to merge.
A second model reviewing the first one shares its blind spots. A graph query gives the same answer every time and shows its reasoning as a path you can open.
AI work scales with tasks, not headcount. You pay for what the agents actually compute, on the provider or the hardware you choose.
Rabotnik. Infrastructure and AI harnesses. Postgres Pro core team.
GoCPG and CodeGraph. Code analysis and code property graphs. Postgres Pro, Yandex Managed PostgreSQL.
Business and partnerships. Pilots and enterprise sales.
We run a pilot on your code and your inference and show what the graph finds that your tests do not.